[CI/Build] Bump flashinfer to v0.6.11.post2 (#41711)

Signed-off-by: Artem Perevedentsev <[email protected]>
Co-authored-by: Vadim Gimpelson <[email protected]>

docker/Dockerfile

@@ -639,7 +639,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 # Install FlashInfer JIT cache (requires CUDA-version-specific index URL)
 # https://docs.flashinfer.ai/installation.html
 # From versions.json: .flashinfer.version
-ARG FLASHINFER_VERSION=0.6.8.post1
+ARG FLASHINFER_VERSION=0.6.11.post2
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system flashinfer-jit-cache==${FLASHINFER_VERSION} \
         --extra-index-url https://flashinfer.ai/whl/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.')

docker/Dockerfile.nightly_torch

@@ -217,13 +217,13 @@ RUN pip install setuptools==75.6.0 packaging==23.2 ninja==1.11.1.3 build==1.2.2.
 
 
 # build flashinfer for torch nightly from source around 10 mins
-# release version: v0.6.8.post1
+# release version: v0.6.11.post2
 # todo(elainewy): cache flashinfer build result for faster build
 ENV CCACHE_DIR=/root/.cache/ccache
 RUN --mount=type=cache,target=/root/.cache/ccache \
     --mount=type=cache,target=/root/.cache/uv \
     echo "git clone flashinfer..." \
-    && git clone --depth 1 --branch v0.6.8.post1 --recursive https://github.com/flashinfer-ai/flashinfer.git \
+    && git clone --depth 1 --branch v0.6.11.post2 --recursive https://github.com/flashinfer-ai/flashinfer.git \
     && cd flashinfer \
     && git submodule update --init --recursive \
     && echo "finish git clone flashinfer..." \

docker/versions.json

@@ -68,7 +68,7 @@
       "default": "true"
     },
     "FLASHINFER_VERSION": {
-      "default": "0.6.8.post1"
+      "default": "0.6.11.post2"
     },
     "GDRCOPY_CUDA_VERSION": {
       "default": "12.8"

requirements/cuda.txt

@@ -9,8 +9,8 @@ torchaudio==2.11.0
 # These must be updated alongside torch
 torchvision==0.26.0 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
 # FlashInfer should be updated together with the Dockerfile
-flashinfer-python==0.6.8.post1
-flashinfer-cubin==0.6.8.post1
+flashinfer-python==0.6.11.post2
+flashinfer-cubin==0.6.11.post2
 apache-tvm-ffi==0.1.9
 tilelang==0.1.9
 # Cap nvidia-cudnn-frontend (transitive dep of flashinfer) due to

tests/kernels/moe/test_cutedsl_moe.py

@@ -453,11 +453,15 @@ def test_flashinfer_cutedsl_moe_masked(
     )
 
     # reference
-    a_fp4, a_scale_interleaved = fp4_quantize(hidden_states, input_global_scale)
+    # input_global_scale is per-expert ([num_experts]); fp4_quantize and
+    # dequantize_nvfp4_to_dtype are non-grouped APIs that expect [1] or
+    # [num_tokens]. Use a single element since all values are uniform here.
+    a_global = input_global_scale[:1].contiguous()
+    a_fp4, a_scale_interleaved = fp4_quantize(hidden_states, a_global)
     a_in_dtype = dequantize_nvfp4_to_dtype(
         a_fp4,
         a_scale_interleaved,
-        input_global_scale,
+        a_global,
         dtype=hidden_states.dtype,
         device=hidden_states.device,
         block_size=16,

vllm/model_executor/layers/fused_moe/oracle/mxfp4.py

@@ -907,24 +907,27 @@ def convert_gpt_oss_weight_to_mxfp4_moe_kernel_format(
         else:
             assert mxfp4_backend == Mxfp4MoeBackend.FLASHINFER_CUTLASS_MXFP4_BF16
 
-            def _interleave_mxfp4_cutlass_sm90(w):
-                w_shape = w.shape
-                w_interleaved = w.reshape(w_shape[0], w_shape[1], (w_shape[2] // 4), 4)
-                w_interleaved = w_interleaved.permute(0, 2, 1, 3)
-                w_interleaved = w_interleaved.reshape(
-                    w_shape[0], w_shape[2] // 4, w_shape[1] * 4
-                )
-                return w_interleaved
+            from flashinfer.fused_moe import (
+                interleave_moe_scales_for_sm90_mixed_gemm,
+                interleave_moe_weights_for_sm90_mixed_gemm,
+            )
 
-            w31_scales = w13_scale_swapped.to(torch.uint8)
-            w31_scales_interleaved = _interleave_mxfp4_cutlass_sm90(w31_scales)
-
-            w2_scale = w2_weight_scale.data.to(torch.uint8)
-            w2_scale_interleaved = _interleave_mxfp4_cutlass_sm90(w2_scale)
+            w13_weight_interleaved = interleave_moe_weights_for_sm90_mixed_gemm(
+                w13_weight_swapped.contiguous(), "fp4"
+            )
+            w2_weight_interleaved = interleave_moe_weights_for_sm90_mixed_gemm(
+                w2_weight.contiguous(), "fp4"
+            )
+            w31_scales_interleaved = interleave_moe_scales_for_sm90_mixed_gemm(
+                w13_scale_swapped.to(torch.uint8)
+            )
+            w2_scale_interleaved = interleave_moe_scales_for_sm90_mixed_gemm(
+                w2_weight_scale.data.to(torch.uint8)
+            )
 
             return (
-                w13_weight_swapped,
-                w2_weight,
+                w13_weight_interleaved,
+                w2_weight_interleaved,
                 w31_scales_interleaved,
                 w2_scale_interleaved,
                 w13_bias_swapped,